Thank you David for your answer, - grade2 is a factor with 2 categories: "high" and "low" - yes as.factor is superfluous; it is just that it avoids warnings sometimes. This can be overlooked. - I will look into Terry Therneau answers; he gives a good explanation on how to obtain the hazard for an individual given a set of predictors for the Cox model; I will look to see if this works for survreg andlook into survreg.distributions if it doesn't - I'll come back if I can't figure it out.
Thanks again. Best, David Biau. ________________________________ De : David Winsemius <dwinsem...@comcast.net> Cc : r help list <r-help@r-project.org> Envoyé le : Sam 13 novembre 2010, 19h 55min 10s Objet : Re: [R] interpretation of coefficients in survreg AND obtaining the hazard function for an individual given a set of predictors On Nov 13, 2010, at 12:51 PM, Biau David wrote: > Dear R help list, > > I am modeling some survival data with coxph and survreg (dist='weibull') using > package survival. I have 2 problems: > > 1) I do not understand how to interpret the regression coefficients in the > survreg output and it is not clear, for me, from ?survreg.objects how to. Have you read: ?survreg.distributions # linked from survreg help > > Here is an example of the codes that points out my problem: > - data is stc1 > - the factor is dichotomous with 'low' and 'high' categories Not an unambiguous description for the purposes of answering your many questions. Please provide data or at the very least: str(stc1) > > slr <- Surv(stc1$ti_lr, stc1$ev_lr==1) > > mca <- coxph(slr~as.factor(grade2=='high'), data=stc1) Not sure what that would be returning since we do not know the encoding of grade2. If you want an estimate on a subset wouldn't you do the subsetting outside of the formula? (You may be reversing the order by offering a logical test for grade2.) > mcb <- coxph(slr~as.factor(grade2), data=stc1) You have not provided the data or str(stc1), so it is entirely possible that as.factor is superfluous in this call. > mwa <- survreg(slr~as.factor(grade2=='high'), data=stc1, dist='weibull', > scale=0) > mwb <- survreg(slr~as.factor(grade2), data=stc1, dist='weibull', scale=0) > >> summary(mca)$coef > coef > exp(coef) se(coef) z Pr(>|z|) > as.factor(grade2 == "high")TRUE 0.2416562 1.273356 0.2456232 > 0.9838494 0.3251896 > >> summary(mcb)$coef > coef exp(coef) > se(coef) z Pr(>|z|) > as.factor(grade2)low -0.2416562 0.7853261 0.2456232 -0.9838494 > 0.3251896 > >> summary(mwa)$coef > (Intercept) as.factor(grade2 == "high")TRUE > 7.9068380 -0.4035245 > >> summary(mwb)$coef > (Intercept) as.factor(grade2)low > 7.5033135 0.4035245 > > > No problem with the interpretation of the coefs in the cox model. However, i do > not understand why > a) the coefficients in the survreg model are the opposite (negative when the > other is positive) of what I have in the cox model? are these not the log(HR) > given the categories of these variable? Probably because the order of the factor got reversed when you changed the covariate to logical and them back to factor. > b) how come the intercept coefficient changes (the scale parameter does not > change)? > > 2) My second question relates to the first. > a) given a model from survreg, say mwa above, how should i do to extract the > base hazard Answered by Therneau earlier this week and the next question last month: https://stat.ethz.ch/pipermail/r-help/2010-November/259570.html https://stat.ethz.ch/pipermail/r-help/2010-October/257941.html > and the hazard of each patient given a set of predictors? With the > hazard function for the ith individual in the study given by h_i(t) = > exp(\beta'x_i)*\lambda*\gamma*t^{\gamma-1}, it doesn't look like to me that > predict(mwa, type='linear') is \beta'x_i. > b) since I need the coefficient intercept from the model to obtain the scale > parameter to obtain the base hazard function as defined in Collett > (h_0(t)=\lambda*\gamma*t^{\gamma-1}), I am concerned that this coefficient > intercept changes depending on the reference level of the factor entered in the > model. The change is very important when I have more than one predictor in the > model. > > Any help would be greatly appreciated, > > David Biau. > David Winsemius, MD West Hartford, CT [[alternative HTML version deleted]]
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
